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Ecological Economics 5

ANALYSIS

Conserving energy in smallholder agriculture: A multi-objective

programming case-study of northwest India

Samarthia Thankappana, Peter Midmoreb,T, Tim Jenkinsc

aCentre for Business Relationships, Accountability, Sustainability and Society, Cardiff University, CF10 3AT, UKbSchool of Management and Business, The University of Wales Aberystwyth, SY23 3DD, UK

cInstitute of Rural Studies, The University of Wales Aberystwyth, SY23 3AL, UK

Received 2 September 2003; received in revised form 20 January 2005; accepted 21 January 2005

Available online 3 May 2005

Abstract

In semi-arid conditions in Northwest India, smallholder agriculture has made increasing use of subsidised mechanisation and

energy inputs to reduce short-term risks. However, detrimental environmental consequences have occurred, not least a rapidly

falling water table, and energy-intensive production is threatened by the prospect of increasing scarcity and expense of energy

supplies, especially as urban demands are forecast to grow rapidly. This paper describes the energy flows through four

subsystems of smallholder agricultural villages: the crop system; non-crop land uses; livestock systems; and households. It

employs a multi-objective programming model to demonstrate choices available for maximands either of net solar energy

capture or financial surpluses. Applied to three villages selected to represent major settlement types in the Saurashtra region of

Gujarat, the results demonstrate that both energy conservation and financial performance can be improved. Although these

results need qualifying because of the reductionist, linear character of the model used, they do provide important insights into

the cultural role of mechanisation and the influence of traditional agricultural practices. They also underline the need for local

energy conservation strategies as part of an overall approach to improved self-determination in progress towards rural

sustainability.

D 2005 Elsevier B.V. All rights reserved.

Keywords: Energy conservation; Smallholder agriculture; Optimising model; India

1. Introduction

Energy is a fundamental ingredient in the process

of economic development, as it provides essential

0921-8009/$ - see front matter D 2005 Elsevier B.V. All rights reserved.

doi:10.1016/j.ecolecon.2005.01.017

T Corresponding author.

E-mail address: [email protected] (P. Midmore).

services that maintain economic activity and the

quality of human life. Thus, shortages of energy are

a serious constraint on the development of low-

income countries (WEC, 2000; IEA, 2001). Shortages

are caused or aggravated by widespread technical

inefficiencies, capital constraints and a pattern of

subsidies that undercut incentives for conservation.

Also, the world is likely in the medium to long term to

6 (2006) 190–208

S. Thankappan et al. / Ecological Economics 56 (2006) 190–208 191

become increasingly energy-scarce, particularly in oil.

As easily recoverable supplies dwindle, unconven-

tional sources such as oil shale and conversion from

coal will become important, prices will rise and the

probability of supply disruptions and price shocks will

increase (IEA, 1998).

India was the world’s sixth largest consumer of

commercial energy in 1998—its use of 270.6 million

tonnes of oil equivalent corresponded to about 13%

of total commercial US consumption (BP Amoco,

1999). In absolute terms it is clearly highly depend-

ent on global energy resources, although obviously

per capita consumption is much lower (WEC, 2000).

Indian commercial energy supplies come primarily

from local coal stocks, which contributed 57%; oil,

of which over two-thirds is imported, contributed

32%; and the remaining 11% was derived from

natural gas, hydroelectricity and nuclear sources

(TERI, 2000).

However, although India, in common with other

low-income countries, has much lower per capita

energy consumption than high-income countries, it

correspondingly experiences energy shortages in

meeting its growing industrial and household

demands. Urban centres face worsening environ-

mental problems from rapid and continued growth

in the use of fossil fuels due to rising incomes and

greater access to consumer products among middle

and upper income groups. Over recent decades,

shortages of coal and electricity have progressively

increased, and various barriers impede progress

towards energy efficiency including, conspicuously,

subsidies that keep energy prices artificially low.

According to a report from IIASA (1998), energy

shortages are compounded by disparities in economic

activity and living standards, and a priority for

decision-makers concerned with sustainable energy

development in all countries is to extend access to

commercial energy services to people who do not

have it, and to coming generations who are likely to

be similarly deprived. This problem is most acute in

rural areas, where traditional fuels meet a quarter of

India’s total energy needs. Shortage of these has led

to their substitution with lower quality fuels such as

crop residues and dung cake, and greater efforts to

extract fuel wood, with consequential degradation of

vegetation cover (Qureshi and Kumar, 1996). Rapid

population growth requires either intensified agricul-

tural production or a substantial increase in cropped

area, both of which imply an increase in energy

input. Rural–urban migration trends suggest that,

soon, insufficient additional human labour will be

available in rural areas; and there is also little

possibility of an increase in the availability of animal

power, due to a slow reduction in pasturage area,

competition for survival between humans and ani-

mals, and the increasingly uneconomic energetics of

bullock farming (Rao, 1984). Hence, the energy

shortfall may largely have to be made up by external

sources of power for electric pumps, tractors and

power tillers.

However, since growth in output is increasingly

dependent on limited fossil fuel reserves, it seems

that b. . .current agricultural techniques are unsus-

tainable in the long run . . . since present consump-

tion of fossil energy has the effect of reducing

consumption availability to future generationsQ(Conforti and Giampietro, 1997: 232). Thus, future

energy scarcity is likely to radically alter price

ratios, making it important to identify priorities to

improve the efficiency and sustainability of small-

holder agro-ecosystems in the subcontinent by

minimising energy use whilst at the same time

maintaining and (if possible) enhancing incomes.

Continuation of the present technical path of

agricultural development risks, at some stage,

serious impairment of the incomes of poor rural

people and their food security.

Our aim in this paper is to explore the possibility

of maximising both net revenue and energy returns

in the agricultural sector at village level to fulfil

food, fuel and feed requirements. By using a multi-

objective programming approach, trade-offs between

optimal solar energy capture and financial surpluses

are investigated; and changes in agricultural activ-

ities required to optimise energy use are explored to

determine whether economic conditions and local

energy utilisation of the village can be improved,

and energy imports reduced. This technique is

applied in the context of villages practicing small-

holder agriculture in a semi-arid region of northwest

India. The section following this introduction

reviews energy studies in agriculture, particularly

in the context of low-income countries. The next

sketches the energy budgets of three representative

villages chosen as case studies for further analysis.

S. Thankappan et al. / Ecological Economics 56 (2006) 190–208192

The subsequent section sets out the framework of

the modelling exercise undertaken to improve

understanding of structural and dynamic aspects of

semi-arid rural agro-ecosystems, as they currently

exist, and to highlight both the long-term unsustain-

ability of agriculture as practised in the region, and

the implications of unsustainable energy use. The

penultimate section reviews the results of the multi-

criteria programming model, and the final section of

the paper concludes by reflecting on the implica-

tions for policymakers and farmers concerned with

the long-run sustainability of agriculture in the

region.

2. Energy studies in agriculture

The importance of energy in agriculture was

underlined by the dramatic increase in oil prices in

1973, when it began to be appreciated that most

agro-ecosystems powered by fossil fuel in most

high-income countries were outstandingly energy

inefficient. For example Stanhill (1984) estimated

that intensive agricultural systems in high-income

countries required inputs of more than 10–12 units

of fossil fuel energy to produce 1 unit of food

energy output. Therefore, many subsequent studies

have tried to formulate appropriate strategies for

agricultural development in an energy-expensive

future.

This concern drew attention to earlier pioneering

studies providing a comparative examination of the

energetics of the world food production system,

carried out by Transeau, (1926), Odum, (1967),

Pimentel, (1980). Traditional smallholder village

ecosystems are sometimes thought to have low

productivity due to a lack of external inputs and the

inefficiency of human and animal power, for which

the popular solution is increased mechanisation.

However, Singh and Chancellor (1974, 1975) have

shown that mechanisation does not necessarily

increase productivity This is supported by Lee

(1979), who reported that rice yields obtained from

intensively mechanised farming in Japan were less

than those of almost completely animal powered rice

production in Korea. Similarly, Bhatia (1977) dem-

onstrated that, for India, there is no cost or environ-

mental advantage in using mechanical power, such as

tractors, rather than bullock labour input. Close study

of animal powered village agro-ecosystems in Asia

and Africa suggests that village ecosystems consis-

tently achieve higher yields than equivalent large

mechanised farms, when nutrient inputs and other

biotic factors are similar (Moerman, 1968; Chandler,

1979; Wortman, 1980).

With regard to energy efficiency, in Meghalaya, in

north-eastern India, Mishra and Ramakrishnan

(1982) carried out village ecosystem analysis for a

typical khasi village ecosystem under slash and burn

agriculture (jhum agriculture) at higher elevations,

and concluded that although high energy input–

output ratios were achieved, this depended critically

on already overexploited renewable forest resources,

and that horticulture, plantation and fuelwood pro-

duction systems might be preferable. Kumar and

Ramakrishnan (1989) also studied the village eco-

system function of some tribal societies of north-

eastern India, in which jhum cultivation and its effect

on environmental degradation, energy efficiency and

crop production were considered. Their study

revealed that a major input in shifting agriculture

was human labour and that the energy efficiency (i.e.

the net energy yield) of animal husbandry was low.

Despite this, most food requirements of domestic

subsystems were met from shifting agriculture,

except for rice, which was imported to the village

ecosystem. Pandey and Singh (1984) conducted a

detailed study of three villages in the central

Himalayas to investigate the energy efficiency of

agricultural ecosystems with respect to agricultural

activity. Although the study revealed that agriculture

in these villages was energy efficient in terms of net

energy yield, agronomic production coupled with the

milk yield from the animal system was not enough

to meet minimum energy requirements of villagers

and, as a result, food-grains were imported from the

market.

Traditionally, farmers have relied on experience,

intuition and comparisons to make their decisions. It

is only that, comparatively recently, widely available

personal computers have developed to allow math-

ematical programming support for farm planning in

more complex situations. These may be of some use

in assisting farmers to adapt efficiently to a changing

economic and technological environment. Coupled

with the various studies of rural energy use in India,


several models have been developed, by Joshi et al.

(1992), Sinha and Kandpal, (1992), Srinivasan and

Balachandra, (1993), Painuly et al. (1995), Malik et

al. (1994), Raja et al. (1997) and Parikh and

Ramanathan (1999a,b). In all these studies the

traditional linear programming approach was used,

seeking to maximise a single objective function and

focusing on household energy at regional or macro

level models.

However, hardly any such studies have been

carried out in the villages of semi-arid agro-climatic

zone of India, where water scarcity and high temper-

ature are limiting factors for agricultural operations.

Equally, it is clear that, whilst energy efficiency is of

fundamental importance when considering long-term

sustainability, recommendations that would lead to

farmer impoverishment stand little chance of being

adopted. Therefore, policymakers and advisors may

almost certainly encounter multiple objectives with a

need for simultaneous optimisation. As it is difficult

(normally impossible) to optimise all objectives

together, a preferred trade-off between several objec-

tives is the best that can be achieved. One way of

exploring trade-offs is the multiple objective pro-

gramming technique, which seeks, subject to a given

set of constraints, to find a subset of efficient solutions

from amongst an entire set of feasible solutions. Some

energy studies of this type have been conducted in

India, prominent among which are those of Chetty

and Subramanian (1988), Ghosh et al. (1995) for

rural India as a whole and Singh et al. (1996) for a

Punjab village. Given the lack of attention in the

literature to the semi-arid agro-climatic regions of

India, this paper focuses on the water-scarce region

in the western peninsular region of Gujarat, which

experiences frequent droughts and practices energy-

intensive agriculture. In such circumstances, a study

focusing on energy–agriculture linkages assumes

considerable significance.

3. The energy situation of the case-study villages

An essential pre-requisite for modelling improve-

ments in energy and financial efficiency is the

identification of resource flows at village level for

a semi-arid agro-ecosystem. The context of the

analysis is the agriculture practised the Saurashtra

sub-region, which contributes over 25% of total

Indian groundnut production. Although smallhold-

ings are predominant, mechanisation is becoming

increasingly common, supported by state subsidies

for tractors and other agricultural equipment. The

region lies between 20842V and 2833V N latitude and

6984V and 72818V E longitude. The climate is hot,

with rainfall of between 550–1000 mm annually. The

sub-region is surrounded by sea on three sides, and

the central part is hilly (although only a small

portion has an elevation of 300 m above mean sea

level) and has undulating terrain extending into

plains towards the seashore. Two districts (Bhavna-

gar and Rajkot) were chosen as typical of the

agriculture of the region as a whole (see Fig. 1).

The major source of data for this study originates

from farm surveys conducted in 18 villages within

these two districts, chosen by a multi-stage random

sampling procedure.

Drawing on the concept of the farm model for

energy dynamics suggested by Odum (1983) and

Han et al. (1985), four subsystems of the village

ecosystem have been identified and explored: the

crop subsystem, the non-crop subsystem (involving

low intensity grazing land), the animal subsystem

and the human subsystem. Variables allowing the

identification of major energy and financial flows for

each subsystem were collected by questionnaire for

the production season of 1997/1998 (Table 1

provides summary information for crop inputs:

further details can be found in Thankappan, 2003).

Because of the sheer volume of data, three repre-

sentative villages have been chosen from this survey

by hierarchical agglomerative cluster analysis involv-

ing variables describing land area and village size,

standard deviation of holding area and energy

intensity. The villages closest to the cluster means

at the three-cluster level of aggregation were selected

in an attempt to represent different types of energy

use.

Baghi is the smallest village of the three, encom-

passing 637 ha and, with a population of 1307, it had

the highest population density. However, the distribu-

tion of land holdings was the least unequal of all the

villages. 40% of the vegetables produced from the

crop subsystem, 92% of the oilseeds and the entire

cotton produced were sold outside the village. From

the animal subsystem, households consumed 42% of

Fig. 1. Bhavnagar and Rajkot districts of Saurashtra region: location map.


the total milk produced, and 50% of the total dung

production was used as fuel1. There was a deficiency

of food-grains. In the survey year, total energy

(including household commodities) imported into

the village ecosystem amounted to 7199 GJ annually

while the net total energy exported amounted to

18,135 GJ, In all the three villages, the energy imports

included seeds for the next season, food-grains to

make up for the shortage if any, cattle feed and fossil

fuel-based imports included fertilisers, diesel for

irrigation pumps and tractors. The energy exports in

the villages included mainly crop products like

oilseeds, cotton, vegetables and animal products

included milk and wool.

1 Half the dung collected from animals was recycled back into the

agro-ecosystem, while other half was dried and used as household

fuel. The women and children generally responsible for livestock

ensure the collection of dung while animals graze.

Jamanvav is the largest village, with the least equal

distribution of land holdings, and covers 1576 ha with

a population of 1934. Groundnut is the important cash

crop of the village, accounting for about 35% of the

total cultivated area. Other kharif 2 crops cultivated in

the village were cotton, pearl millet and sesame.

Sorghum was cultivated for fodder, together with

alfalfa, which is grown throughout the year with

repeated harvests. Rabi3 crops grown in the village

included mustard, gram and wheat. Vegetables were

also cultivated in a marginal piece of land. The overall

performance of the village ecosystem shows 47% of

the vegetables produced from the crop subsystem,

95% of the oilseeds and the entire cotton crop were

sold outside the village. From the animal subsystem,

2 Summer season (March to September).3 Winter season (October to February).

Table 1

Annual energy inputs (MJ/ha)

Pearl millet Wheat Gram Groundnut Sesame Mustard Cotton Sorghum

Baghi

Seeds 132.83 2432.28 1048.78 3248.70 72.74 138.62 55.91 473.56

Human labour 5.60 0.61 9.14 2.85 8.620 8.38 5.78 4.30

Animal labour 11.56 2.66 32.32 2.30 21.537 17.89 30.96 10.51

Machine labour 37.75 10.82 30.25 73.73 26.125 30.74 54.26 79.02

FYM – – 140.08 101.96 – – – –

Chemical fertilisers 7.20 1.76 22.85 10.93 10.495 11.51 113.65 6.29

Pesticide – – 10.99 – – 4.58 23.47 –

Irrigation 8.38 3.51 18.74 12.12 – 23.10 60.82 15.20

Jamanvav

Seeds 115.44 3774.09 1032.89 1727.81 60.21 123.60 51.63 4310.83

Human labour 8.89 34.31 23.85 19.90 17.55 16.40 26.63 11.25

Animal labour 14.95 74.77 80.56 20.05 24.17 49.35 19.76 10.73

Machine labour 266.11 872.12 453.00 358.33 388.94 334.11 1125.79 271.49

FYM – – 712.66 271.66 – – – –


Pesticide – – 20.42 – – 18.71 11.13 –

Irrigation 39.31 335.66 97.56 26.83 – 33.19 54.78 36.36

Kundaich

Seeds 124.16 2370.71 1048.75 3080.66 64.37 138.67 55.97 454.67

Human labour 110,318.99 183,531.28 2802.07 318,098.29 13,902.45 13,867.36 16,311.93 109,259.94

Animal labour 1,000,707.65 1,514,864.00 5051.90 405,034.30 197,793.25 19,153.32 18,159.82 1,096,497.35

Machine labour 3,792,173.96 7,332,468.89 1654.77 6,739,658.42 12,208.22 15,040.60 16,358.79 4,978,438.34

FYM – – 6142.30 6366.20 – – – –


Pesticide 939.39 1698.46 2737.75

Irrigation – 7,534,354.68 – 872,074.34 1,470,926.19 1,361,101.33 –


76% of the total milk produced was consumed by the

human subsystem, with the remainder, together with

total wool production, exported to the market. In the

survey year, 19,485 GJ of energy in total were

imported in to the village ecosystem, while the

exports amounted to 41,386 GJ.

Kundaich is also a large village, with a land area of

1434 ha and a population of 1529, but it has a less

skewed distribution of landholdings and far more

uncultivable land than either Baghi or Jamanvav.

Groundnut was the major kharif crop grown, in about

74% of the cultivated area, while the other kharif

crops were cotton, sesame, sorghum and pearl millet.

During the rabi season, wheat, mustard and gram were

cultivated, while vegetables were grown throughout

the year with about 3 harvests per year. The entire

food-grain output of the village was consumed in the

village. Of the total vegetables produced, 47% were

consumed within the village, while the rest were

marketed. 8% of the total oilseeds produced were

retained as seed, and the rest exported to the market,

and households consumed 65% of milk production. In

the survey year, imports of energy to the village

ecosystem amounted to 10,785 GJ while total exports

amounted to 71,785 GJ.

Detailed analysis of financial flows in the three

villages reveals positive returns over direct cash

expenses for crop subsystems in two villages, Baghi

and Kundaich, while in Jamanvav the crop subsystem

shows a substantial financial loss. Reasons for the

high expenses in this village could be attributed to the

increased spending on fossil fuel-based inputs and

expenses on labour. Revenue from the crop subsystem

was higher in Kundaich than in the other two villages,

as a large percentage of fodder produced was sold

externally, boosting total sales.

Division of the expenses incurred in the crop

subsystem reveals that fossil fuel-based inputs formed


a major component in the villages Baghi and

Kundaich (96% and 64% respectively). Human

labour4 contributed less than 2% of cash expenses

in Baghi, although rather more, 36%, in Kundaich.

In the case of the village Jamanvav, the share of

fossil fuel-based inputs was less (27%) and labour

expenses were relatively high (73%). It might be

argued that the financial returns from each village

reflect a positive correlation between the fossil-based

inputs and financial returns, significant from the

perspective of the long-term sustainability of the

region’s agriculture.

4. Framework of the modelling exercise

The major consideration when formulating the a

problem within a mathematical programming frame-

work is to identify the forces that shape the

productivity of energy use in village ecosystems

and thereby estimate the changes in agricultural

activities that are required to optimise energy use.

We have chosen to model activities at village level as

a means of simplifying the framework: this avoids the

problem of accounting for inter-household transfers

within the village, but also reflects the fact that

custom and the influence of the village headman are

important determinants of this type of smallholder

agriculture. We also examine the interplay between

two objectives: to date, researchers have mostly

modelled decision-making either on the basis of

economic criteria or energy efficiency. Finally, to

focus the exercise, households have not been

incorporated directly into the model; rather, only

the points of interchange between them and the

livestock, crop and non-crop systems have been

accounted for, so that the quantitatively larger

interactions taking place within the agricultural

systems can be focused on. Fundamentally, the

programming problem involved in modelling the rural

energy system of a village and emerging consumption

patterns involves finding a suitable way of cultivating

m crops and rearing n livestock by using r external

4 In these villages, most farm labour is contributed by households

and very little is hired; consequently, energy and financial inputs

diverge significantly.

inputs corresponding to energy needs whilst meeting,

at the same time, the objectives of maximising net

energy outputs from the village agro-ecosystem and

maximising financial net revenue from the village

agro-ecosystem.

Formally, the maximands are:

Z1 ¼X11j¼1

Ciei þX3j¼1

Ajej �X4k¼1

Mkek ð1Þ

Z2 ¼X11i¼1

Ciyipi þX3j¼1

Ajyjpj �X4k¼1

Mkpk ð2Þ

Z1 is diverted solar energy flow, Ci represents the ith

land use, in hectares; Aj the jth livestock type, in head

of animals; and Mk the kth external input in tonnes of

fertiliser, pesticides and feed and litres of diesel fuel.

The coefficients ei, ej and ek convert crop and

livestock outputs and external inputs into energy

values expressed in megajoules. Z2 is total income.

The set of coefficients pi, pj, pk denote prices per ton,

in rupees, of each commodity while the coefficients

yi, and yj denote the output of each commodity per

hectare or per head.

The constrained optimisation of these maximands is

based on the resource limits of the villages, in particular

the land area, in which availability of land and crop

rotation have been used to capture the effect of

cropping intensity, and also the local needs of the

village population and its livestock. All seed require-

ments are met from purchased inputs, except ground-

nut, where customarily seeds are saved from the

previous season’s crop. We have dealt with this by

estimating the net yield of crops after seed inputs have

been accounted for.

Nutrient requirements are supplied through the

recycling of animal waste and from nitrogen fixed by

leguminous crops, offsetting the need for chemical

fertiliser purchases by the village. Human and animal

labour availability determine crop labour intensity.

Due to the abundance of labour in the region’s

villages, it has been assumed that the opportunity

cost of labour is minimal such that labour is

immobile in the short run, and there are no purchases

of labour input from within or outside the village. In

the village agro-ecosystem, animals play a dual role,


cows and buffaloes providing milk, while bullocks

supply animal power. Both groups consume fodder

and produce manure and therefore need to be related

through the constraints to crop production and

external energy input. The constraints applied to

the maximands in Eqs. (1) and (2) are as follows.

Land use is limited to the total land available in the

village, and uncultivable land is constrained to be a

minimum of the existing area of the wasteland,

effectively constraining the total potential cultivable

area to a maximum. This constraint has been set since,

generally and by definition, it is not possible to bring

the wasteland under cultivation.

X10i¼1

CiVTCL ð3Þ

where TCL is the total cultivable land area available

in the village.

Legume crops are an important component of the

crop rotation cycle, and are grown approximately one

year in three; therefore, the crop constraint has been

set such that non-leguminous crops may not exceed

70% of the land area.

X7i¼1

NLCiV0:7� TCL ð4Þ

where NLCi is the land area of the ith non-leguminous

crop. Oilseed crops are also fundamental in the

medium term, because of the region’s economic

dependence on them; hence these are constrained to

be a minimum of 50% of the crop area.

X3i¼1

OLiV0:5� TCL ð5Þ

where OLi is the land area of the ith oilseed crops.

Although sesame and mustard are important oilseed

crops, their acceptance is limited due to agronomic

problems; therefore, a balance is required such that

the area of groundnuts (considering the fact that

groundnut is an important crop of this region) is

restricted to that of the total area under the other two

oilseed crops.

OLg � OLs � OLmz0 ð6Þ

where, respectively, OLg is the area under groundnut,

OLs, the area under sesame and OLm, the area under

mustard.

Vegetables are cultivated on accessible land in the

village, primarily for local consumption, although

after meeting the local needs any remainder is

exported. The area under vegetables is therefore fixed

at current levels.

Considering the fact that there is an abundance of

labour (human as well as animal) in the villages in the

Saurashtra region, the sum of human and animal

labour requirements of each crop type is restricted to

total availability in the village. Since women are

normally responsible for the care of the livestock,

each animal type is limited to 20% increase over the

current level, on the assumption that this would be the

maximum acceptable with the present gender division

of labour.

Xnj¼1

AjV1:2ATj ð7Þ

where A*j denotes the current total of each livestock

type.

Crop nutrient needs have been converted to the

energy equivalent of manufactured nitrogen input,

because nitrate input can, broadly, be assumed to

represent the most significant energy flow involved.

These nitrogen needs of the crops are met from animal

manure and nitrogen fixed by the leguminous crops

cultivated in the village, with the residual provided by

external purchased inputs of fertilisers.

X10i¼1

Cidi �X3j¼1

Ajdj �Mdz0 ð8Þ

where the net nitrogen requirements per hectare of the

ith crop (after allowing for fixation by leguminous

crops) is di; the amount of nitrogen derived from

manure per head of the jth animal type is dj; and the

total purchased nitrogenous fertiliser is Md.

Animal feed requirements are met from a range of

sources: crop by-products, fodder crops, and supplies

collected from wasteland.

X3j¼1

Aj fj �X6i¼1

Ci fi �Mf V0 ð9Þ


where fj is the individual fodder consumption

requirement of the jth animal type; fi, the fodder

obtained (either directly or as a residue) from each

hectare of the ith crop; and Mf, purchased fodder

inputs. Grazed fodder is an important constituent in

cattle and buffalo diets, and consequently the area of

wasteland provides an important constraint on animal

numbers; fj is the minimum fraction of total feed

required from wasteland.

C11 f11 �Xnj¼1

Ajxj fjV0 ð10Þ

Only a fairly small quantity of pesticides5 is used

to control aphid attacks on gram, mustard and cotton,

and the requirements of these are set equal to, or less

than, total purchases. Similarly, the machine labour

requirements involved in cultivation and irrigation

have been expressed in terms of diesel fuel inputs

per hectare, and are also set equal to, or less than,

total purchases. We have chosen not to model

households directly, for two reasons: firstly, because

the magnitude of energy flows involved are rela-

tively small, compared to the crop and livestock

systems; and secondly, because households overlap

with larger systems, whose effect is to add unneces-

sary complexity into modelling the sustainability of

the agro-ecosystem. However, to complete the

account, the removal of fuels from crop residues,6

animal manure and wastelands need to be included.

The total household requirement for energy in each

village is derived from the overall population

(estimated to be, on average, 428 MJ per person,

following Painuly et al. 19957), and is set equal to,

or less than, fuel derived from crop residues and

animal dung. The majority of requirements for

household energy were primarily met from crop

residues and animal dung; kerosene and biogas are

6 The woody part of the crop plants especially from crops like

cotton and sesame are collected from crop fields and used as

household fuel, while the dried leaves are left to augment soil

organic matter.7 Painuly et al. suggested this figure for the southern state of

Karnataka; we assume that there is no significant variation in the

socio-economic status between the two regions.

5 Pesticide usage in Baghi, Jamanvav and Kundaich in the year of

the survey was 0.003, 0.003 and 0.005 t/ha, respectively.

used in a few households, but their contribution,

being negligible, has been ignored.

TPdRð Þ �X3i¼1

Cixi �X3j¼1

Ajvj �WxV0 ð11Þ

where TP is the population of the village, and R the

annual energy requirement per person; xi, the fuel

energy obtained, per hectare, from the ith crop residue;

xj, the fuel energy per head from the dung of the jth

livestock type; and Wx, fuel energy obtained from the

wood collected from the wastelands.

Consolidating this section, and providing the data

used in the examples reported in the next section,

constraint matrices for each village are appended to

this paper.

5. Trade-offs between solutions for energy and

financial optimisation

Multi-Objective Programming (MOP) problems

can be tackled in a variety of ways: for example,

as a modified form of goal programming, formu-

lating a minimax objective from weighted devia-

tions from the solutions of the respective single

objective function problems. However, as Kalrath

and Wilson (1997) suggest, great care is required

in selecting the nature and form of target variables,

and simpler approaches may be preferred. In

essence, the MOP approach replaces the traditional

concept of optimality with that of efficiency, such

that

EffP

Z xð Þ ¼ Z1 xP

� �; Z2 x

P

� �; . . . ; Zq x

P

� �h i

subject to : xPa F

Pð12Þ

where Eff indicates the search for efficient solutions,

and F is the feasible set. There are a number of

methods by which the efficient set may be calculated;

the simplest (especially in a problem involving two

objective functions) is the constraint method (Romero

and Rehman, 1989) where one of the objective

functions is maximised while the other is parameter-

ised and acts as a constraint. For example, when

energy is optimised, parameter values for the financial

0

2

4

6

8

10

12

0 10 3020 40 50 60 70 80

A

B1

B2

C

Q

R

1000 GJ

Rs

mill

ion

Fig. 2. Efficiency frontier: Baghi. Legend: A (Maximum Net Revenue), B1 (Turning Point 1), B2 (Turning Point 2), C (Maximum Energy

Surplus), Q (Existing Position), R (Ideal Point).


constraint range from the total income implied by an

optimal solution for energy alone, up to the total

income implied by a financially optimal solution

alone. This procedure is then reversed, allowing the

efficient frontier set to be traced out.

Figs. 2–4, in turn, plot pairs of values of total

energy and financial surpluses comprising the

efficient set for each village (constraint matrices are

appended to the paper). For comparison, each figure

shows values for the existing position (labelled Q)

0

2

4

6

8

10

12

14

16

18

0 20 40 60 80 101000

Mill

ion

Rs

A

Q

Fig. 3. Efficiency frontier: Jamanvav. Legend: A (Maximum Net Revenue

Position), R (Ideal Point).

and the bidealQ (the values, outside the feasible set,

of the model optimised for energy capture and net

revenue alone, labelled R). Assuming that equal

weight is given to each objective, it is a simple

matter to drop a perpendicular from point R to the

efficient set, and moving from that point along the

set shifts the balance of influence given to either one

or other objective.

However, it is perhaps more interesting to examine

the behaviour of the underlying activities as the trade-

0 120 140 160 180 200 GJ

B1

C

R

), B1 (Turning Point 1), C (Maximum Energy Surplus), Q (Existing

0

2

4

6

8

10

12

14

16

0 20 40 60 80 100 120 140 160 1801000 MJ

Rs

mill

ion B1

C

R

Q

Fig. 4. Efficiency frontier: Kundaich. Legend: A (Maximum Net Revenue), B1 (Turning Point 1), C (Maximum Energy Surplus), Q (Existing

Position), R (Ideal Point).


off between net revenue and energy maximisation

shifts, and for each village this set has at least one (in

the case of Baghi, two) turning points where this

nature changes. The labelled points along the frontier,

respectively, represent maximum net revenue (A),

turning points, (B1, and B2 in the village Baghi), and

maximum energy surplus (C).

The frontier for Baghi is the most classical in form,

with increasing amounts of one objective having to be

given up in order to obtain the other; however,

Kundaich, and more especially Jamanvav, have a

predominantly linear trade-off except for the portion

between points B1 and C, where the amount of

income required to be given up to obtain the optimum

level of net energy capture intensifies sharply. Table 2

describes the trade-off in numerical terms, showing,

for each segment of the efficient set, the amount of net

revenue given up to obtain a specific unit of additional

energy surplus.

Table 2

Trade-offs between net revenue and energy (Rs given up per GJ)

A–B1 B1–B2 B2–C

Baghi 49.83 189.63 1544.74

A–B1 B1–C

Jamanvav 42.15 86.37

Kundaich 48.59 538.07

Table 3 provides original crop areas, livestock

numbers and externally purchased inputs, and com-

pares them with the model’s optimum solutions for

energy and finance at the two maximum points and the

turning points. These provide convenient reference

points around which to describe the changes in

cropping patterns, livestock distributions and input

use. Perhaps unsurprisingly, the existing position is far

from the efficiency frontier, and in each case further

than the frontier is from the ideal point.

In all villages, pearl millet, wheat, gram and

sorghum drop out of the crop rotation altogether,

reflecting their heavy nutrient demand (fertiliser is

costly both in energy terms and finance) and

relatively low energy capture and revenue gener-

ation. Also, no animal feed is purchased, since with

the optimised cropping pattern sufficient feed

resources are available from feed crops, cash crop

by-products and wasteland grazing. Sesame is not

cultivated at all in Baghi, although it appears in

Jamanvav and Kundaich only as revenue max-

imisation receives predominant weighting. Mustard

appears sporadically in the rotations of all three

villages, but it only appears strongly in Jamanvav,

declining after the turning point B1 as the weight of

energy maximisation increases. However, for

groundnut, cotton, and alfalfa, the patterns between

villages are broadly similar. Groundnut, as a crop

which is relatively input-efficient, profitable and

Table 3

Comparison of existing system variables with optima and turning points

Baghi Jamanvav Kundaich

Q A B1 B2 C Q A B1 C Q A B1 C

Energy output (103GJ) 29.7 39.8 63.1 74.0 74.6 48.8 49.8 177.8 186.4 62.1 55.1 161.8 163.0

Total income (106Rs) 4.8 11.1 9.9 7.9 6.9 7.4 15.4 10.0 9.2 8.7 14.6 9.4 8.7

Land use (ha)

Pearl millet 67.7 0.0 0.0 0.0 0.0 144.7 0.0 0.0 0.0 80.4 0.0 0.0 0.0

Wheat 44.2 0.0 0.0 0.0 0.0 19.3 0.0 0.0 0.0 53.2 0.0 0.0 0.0

Gram 14.3 0.0 0.0 0.0 0.0 28.7 0.0 0.0 0.0 17.4 0.0 0.0 0.0

Groundnut 275.8 136.0 136.0 136.0 136.0 322.7 356.1 356.1 356.1 783.2 299.7 299.6 299.7

Sesame 21.6 136.0 0.0 0.0 0.0 48.2 356.1 356.1 0.0 33.5 299.7 299.6 0.0

Mustard 32.6 0.0 136.0 136.0 136.0 64.3 0.0 0.0 356.1 26.5 0.0 0.0 299.7

Cotton 13.9 112.9 0.0 0.0 0.0 219.5 641.0 27.4 0.0 28.0 539.4 539.4 0.0

Sorghum 80.3 0.0 0.0 0.0 0.0 166.6 0.0 0.0 0.0 89.2 0.0 0.0 0.0

Alfalfa 23.7 79.2 193.0 271.3 271.1 21.8 0.0 683.5 710.9 38.9 42.3 42.3 596.9

Vegetables 0.7 0.0 0.7 0.7 0.7 1.3 0.0 1.3 1.3 2.3 0.0 0.0 2.3

Uncultivated land 89.6 169.5 167.9 89.6 89.6 150.0 221.2 150.0 150.0 231.5 252.6 252.6 235.1

Animal numbers

Cows 63 0 13 518 49 313 132 132 0 168 299 299 0

Buffaloes 120 581 569 42 278 148 0 0 68 175 0 0 138

Bullocks 320 21 19 22 22 246 159 58 53 330 9 9 12

136 343 136 271 277

External inputs

Diesel (103l) 42.3 26.6 31.3 35.9 35.9 34.9 88.3 74.0 94.1 199.8 76.8 76.8 79.1

Fertiliser (t) 51.1 4.1 0.0 0.0 0.0 0.2 36.9 0.0 0 111.8 29.6 30 0.0

Pesticides (t) 0.2 4.0 4.8 4.8 4.8 1.2 22.4 1.0 12.5 3.8 18.9 18.9 10.5

Feed (t) 52.7 0.0 0.0 0.0 0.0 109.5 0.0 0.0 0.0 76.0 0.0 0.0 0.0


rotationally appropriate as a nitrogen-fixing legume,

is a prevalent crop across the efficient set, although

in Jamanvav and Kundaich its area declines as

revenue maximisation is given more weight. Cotton

is an important cash crop, and although absent in all

three villages between points C to B1, it increases in

area in more or less direct proportion to the

emphasis on revenue maximisation towards point

A. Alfalfa, conversely, from a maximum level

between points A and B1, declines towards C.

Uncultivated land increases in Baghi between points

B1 and B2, having been stable between A and B1

and B2 and C, although at a lower level where

energy rather than revenue is maximised. The

reverse pattern appears in Jamanvav, where unculti-

vated land increases as energy maximisation is given

more weight. In Kundaich, the model produces a

broadly stable area of uncultivated land, comparable

with existing levels.

With regard to livestock, far fewer bullocks are

kept than at existing levels, with sufficient numbers

only to meet cultivation requirements. In Baghi,

there is an increase in overall animal numbers,

reflecting the availability of uncultivated land for

grazing, whereas in Jamanvav and Kundaich, lower

overall land availability, relatively, requires a

reduction in land area. Buffaloes and cows have

an inverse relationship, revolving around the turning

points. In Jamanvav and Kundaich, where revenues

are maximised at point A, few if any cows are kept

in the villages; at point C, the reverse is true, with

the changeover occurring at point B1; and in

Jamanvav, as uncultivated land rises to a peak

prior to revenue maximisation, this provides extra

grazing for a corresponding rise in cattle. In Baghi,

the situation is more complex due to the greater

initial availability of wastelands for grazing; buffa-

loes are kept at points A and C, but cows begin to


displace them at point B1 as emphasis gradually

shifts from revenue to energy maximisation, and

rise to a peak at point B2 before falling back again

at C.

Apart from the absence of feed, other external

inputs to the system vary according to the cropping

pattern. Diesel use for cultivations and irrigation

varies fairly closely with the area of cultivated land,

although in all villages, as cotton becomes more

important, to some extent more diesel is required.

No fertiliser is used in Baghi, reflecting the

availability of nutrients from increased numbers of

cattle; in Jamanvav and Kundaich, fertiliser use

parallels the role of sesame in the rotation,

increasing as revenue maximisation becomes dom-

inant. In all three villages, the use of pesticide

increases in line with the share of cotton in the

cultivated area.

Some further explanation of the behaviour of these

variables can be found in an examination of the slack

and binding constraints at each point along the

efficient set. When net revenue alone is maximised,

the availability of sufficient animal feed is a binding

constraint in Baghi and Kundaich, despite its avail-

ability for purchase externally, which suggests that it

is very much more cost-efficient to use fodder crops

and by-products for this purpose; at other points along

the effective set, it is not a binding constraint. This

partly explains the behaviour of cow and buffalo

numbers, but the availability of grazing land is always

a binding constraint on total animal numbers. Sim-

ilarly, plant nutrients form a binding constraint in

Jamanvav and Kundaich at point A, but not otherwise;

other constraints governing the crop rotation become

binding, contributing to an explanation of cropping

patterns. The need for sufficient household fuel

provides a binding constraint in Jamanvav, but only

where energy is maximised at point C; animal labour,

constrained by grazing resources, also provides a limit

in Jamanvav except where net revenues are the

maximand.

This clearly demonstrates that the nature of the

trade-offs between social interests represented by

energy conservation and individual interests repre-

sented by smallholder net revenue varies considerably

according to land resources (both cultivable and

waste), population size, and the way that these interact

to produce specific patterns of exploitation under the

restrictive assumptions that the multi-objective pro-

gramming approach requires.

6. Implications for more sustainable smallholder

agriculture

At the outset, the limitations of this approach ought

to be outlined. Firstly, the case-study approach

adopted gives clues to the identification of principal

constraints and opportunities rather than contributing

to the formulation of general rules and procedures.

Second, even for the villages concerned, the models

described in this paper cannot be prescriptive; their

relatively simple form, particularly the linear character

of the constraints, neglects the complex agronomic

considerations that determine crop rotations and

livestock nutrition. Third, modelling of this type can

never hope to encapsulate the complexities of farmer

or societal decision-making and reduce them to a

finite set of unambiguous optimisable objectives.

Whilst developments in expert systems (see, for

example, McCown, 2002) might, to some extent,

transform the type of information and analysis

presented in this paper into practical recommendations

for specific locations, our main message concerns the

nature and direction of interventions required to make

smallholder agriculture more sustainable, in terms of

both energy conservation and farmer livelihoods.

In simple terms, the results suggest the need for

major changes in current smallholder activity at the

village level. The degree to which changes are

implied depends largely on the availability of

wasteland for livestock ranging, the size of village

populations and the demand for household energy

(primarily consisting of firewood for cooking pur-

poses). An added emphasis on revenue maximisation

features cash crops more strongly in the system

(particularly cotton, a crop with significant demand

for irrigation) and, at a certain point, a switch

between cows and higher-cost, higher milk-yielding

buffaloes occurs. With greater emphasis placed on

energy maximisation, the shift towards less input-

intensive cropping patterns causes, at the extreme, a

sharp fall in incomes and scarcity of household fuel

from crop residues and animal dung.

To some extent, these results may appear to

conflict with the intuition of many anthropological

8 Groundwater utilisation in the region stands at 42.56% and the

groundwater balance in the region is 3363 MCM/year (MoA, 2001).


and sociological developmental specialists that tradi-

tional cultivators are both deconomically rationalT andthe guardians of decological wisdomT (see for example

CRA, 1987; Cleveland, 1998). However, insights

acquired during the survey phase of this study suggest

broad reasons for the divergence between current

practice and the activity patterns potentially required

for overall energy efficiency and net revenue max-

imisation. As elsewhere, Indian farmers derive a sense

of existential security from following the patterns of

tradition which to some extent ensure their rationality

and wisdom (Holt and Laury, 2002; Binswanger,

1980), but which also in times of rapid circumstantial

change (notably in the areas of population growth and

ecological pressure) may slow behavioural adaptation.

As noted, the region is semi-arid, with irregular

rainfall, frequent drought, and limited irrigation

resources and facilities. In such conditions, small-

holders are naturally risk-averse and more concerned

with strategies to minimise crop failure than with

optimisation (Huijsman, 1986).

A further facet of tradition is reflected in the

livestock population at the time of the survey, and

the fact that the model suggests that far fewer draft

animals are needed. Therefore, maximisation of

either model objective will need to contend with

the cultural importance of the cow in the Hindu

religious tradition. In fact, sufficient motive power

exists within the villages from animal labour alone,

and the divergence between current practice and

potential energy efficient patterns lies in the

observed tendency for smallholders, especially

younger ones, to prefer mechanisation to traditional

techniques. To some extent, this contradicts the

adherence to tradition noted in the area of cropping

patterns. Psychologically, however, mechanisation

suggests a form of modernity that heightens self-

esteem and social status, especially as the general

process of development and the broadening of

horizons weaken the traditional bonds of belief and

culture. Mechanisation often appears irrational in a

labour-abundant economy since its economic effect

is to augment the productivity of labour. It is

increasingly recognised, however, that a realistic

understanding of rationality must go beyond

economics.

Current policy either directly or indirectly

subsidises energy-intensive activities, including fer-

tiliser use, mechanisation and provision of electrical

energy for irrigation pumps. In the short term, such

actions might be justified in terms of encouraging

agricultural modernisation, technical innovation and

the spread of the market economy in rural areas.

However, if in the medium term the cost of

maintaining these subsidies becomes unsupportable,

smallholders will be left with a set of technologies

and a productive system unsuited to energy-scarce

circumstances. This is likely to be particularly the

case in terms of water availability. Groundwater

resources are being depleted through over-with-

drawal8 and meager recharge from inadequate rainfall:

to access groundwater, a tube well as deep as 200–300

m now needs to be drilled in this region, and the

groundwater table is depleting at an average rate of 3–

3.5 m every year, and in essence, water is starting to

look very much like a non-renewable resource in this

region, as in most other semi-arid regions with high

population densities. Although in principle it is easy

to argue that energy is being incorrectly priced in

resource cost terms (Majumdar and Parikh, 1996),

elimination of such distortions would have significant

livelihood implications for agriculture as currently

practiced (Singh et al., 1992). Therefore, the implica-

tions for policy appear to involve a number of diverse

and small interventions which, together, encourage

greater overall energy conservation (Parikh et al.,

1996), including greater emphasis on nutrient con-

servation and recycling within the farming system

(see for example Sharma et al., 2001; Parikh, 2000);

improved efficiency in household fuel utilisation

(Parikh and Ramanathan, 1999a,b; Painuly et al.,

1992); and the development of greater drought-

tolerance in the portfolio of crops available to small-

holders. These micro-interventions, in conjunction

with closer alignment of energy prices and the

resource costs of providing energy, might improve

the chances of a sustainable agriculture in an energy-

scarce future, in which both energy efficiency and

smallholder livelihoods can be simultaneously

improved.

Ha Head Litres Tonnes

Pearl

millet

Wheat Gram G’nut Sesame Mustard Cotton Sorghum Alfalfa Vegetables Waste Cows Buffaloes Bullocks Diesel Fertilizer Pesticides Feed

Objective Fn 1

(MJ)

26,147.2 21,096.9 20,312.2 33,746.2 9914.1 38,183.5 54,157.3 72,236.7 225,808.9 283,404.1 50,950.0 1932.4 6405.9 0.0 �38.9 �11,400.0 �363,284 �18,145.0

Objective Fn 2

(Rs)

3600.0 5100.0 8150.0 9800.0 9500.0 9400.0 13,300.0 2000.0 3500.0 6500.0 0.0 8000.0 12,000.0 0.0 �9.8 �5784.7 �325.0 �35,000.0

Total land 1 1 1 1 1 1 1 1 1 1 1 b 633.6

Cultivable land 1 1 1 1 1 1 1 1 1 1 b 544.0

Feed 1.50 0.85 0.00 1.02 0.00 0.00 0.00 1.99 6.40 0.00 2.04 �0.98 �1.68 �0.84 1 N 0

Nutrient �0.045 �0.110 0.030 0.030 �0.045 �0.050 �0.050 �0.045 0.030 0.002 0.002 0.002 1 N 0

Pesticide �0.007 �0.035 �0.035 1 N 0

Fuel 2.53 4.00 3.71 2.580 0.627 0.725 0.5 N 260.06

Non-legumes 1 N 89.6

Oilseed crops 1 1 1 1 1 1 b 380.8

Oilseed crop ratio 1 1 1 N 272.0

Machine labour 1 �1 �1 b 0

Vegetable limit �30.0 �117.2 �44.3 �58.6 �30.0 �88.6 �88.6 �60.0 �58.6 �30.0 1 N 0

Animal limit 1 1 1 b 603

Grazing 2.04 �0.29 �0.59 �0.25 N 0

Animal labour �13 �6 �8 �10 �9 �7 �10 �13 �8 �4 200 N 0

Human labour 370 20 65 685 12 8 140 300 30 105 b 2,240,000

Notes to the constraints: the land, cultivable land and vegetable area constraints are set equal to or less than current areas, whereas the wasteland constraint is set equal to or greater than current area; the feed constraint is set to

equal or greater than zero so that the balance between forage from crop by-products and wasteland and animals nutritional needs is made up by purchased inputs; the same principle is applied to the nutrient, pesticide and

mechanical energy constraints; the area of non-legumes is constrained to be equal or less than 70% of the cultivable area, and the proportion of oilseed crops to be equal or greater than 50% of the cultivable area; the oilseed

crop ratio constraint ensures that groundnut, sesame and mustard are in a 2:1:1 ratio or greater; the animal and human labour constraints are set equal or less than current availability; the grazing constraint ensures that animal

numbers are equal to or less than the available uncultivated land; the animal limit constraint constrains total numbers to a figure equal or less than 120% of the current population; and the fuel constraint is set to be equal or

greater than current household needs.

Appendix A

Table 1. Objective functions and constraint matrix for Baghi S.Thankappanet

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56(2006)190–208

204


Pearl

millet

Wheat Gram G’nut Sesame Mustard Cotton Sorghum Alfalfa Vegetables Waste Cows Buffaloes Bullocks Diesel Fertilizer Pesticides Feed

Objective Fn 1

(MJ)

26,147.2 21,096.9 20,312.2 33,746.2 9914.1 38,183.5 54,157.3 72,236.7 225,808.9 283,404.1 50,950.0 1932.4 6405.9 0.0 �38.9 �11,400.0 �363,284 �18,145.0

Objective Fn 2

(Rs)

3600.0 5100.0 8150.0 9800.0 9500.0 9400.0 13,300.0 2000.0 3500.0 6500.0 0.0 8000.0 12,000.0 0.0 �9.8 �5784.7 �325.0 �35,000.0

Total land 1 1 1 1 1 1 1 1 1 1 1 b 633.6


Feed 1.50 0.85 0.00 1.02 0.00 0.00 0.00 1.99 6.40 0.00 2.04 �0.98 �1.68 �0.84 1 N 0

Nutrient �0.045 �0.110 0.030 0.030 �0.045 �0.050 �0.050 �0.045 0.030 0.002 0.002 0.002 1 N 0

Pesticide �0.007 �0.035 �0.035 1 N 0

Fuel 2.53 4.00 3.71 2.580 0.627 0.725 0.5 N 260.06

Wastelands 1 N 89.6

Non-legumes 1 1 1 1 1 1 b 380.8

Oilseed crops 1 1 1 N 272.0

Oilseed crop ratio 1 �1 �1 b 0

Machine labour �30.0 �117.2 �44.3 �58.6 �30.0 �88.6 �88.6 �60.0 �58.6 �30.0 1 N 0

Vegetable limit 1 1 1 b 603

Animal limit 2.04 �0.29 �0.59 �0.25 N 0

Grazing �13 �6 �8 �10 �9 �7 �10 �13 �8 �4 200 N 0

Animal labour 370 20 65 685 12 8 140 300 30 105 b 2,240,000

Human labour 26,147.2 21,096.9 20,312.2 33,746.2 9914.1 38,183.5 54,157.3 72,236.7 225,808.9 283,404.1 50,950.0 1932.4 6405.9 0.0 �38.9 �11,400.0 �363,284 �18,145.0







Appendix B

Table 2. Objective functions and constraint matrix for Jamanvav S.Thankappanet

al./Ecologica

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56(2006)190–208

205


Pearl

millet

Wheat Gram G’nut Sesame Mustard Cotton Sorghum Alfalfa Vegetable Waste Cows Buffaloes Bullocks Diesel Fertilizer Pesticides Feed

Objective Fn 1

(MJ)

26,147.2 21,096.9 20,312.2 33,746.2 9914.1 38,183.5 54,157.3 72,236.7 225,808.9 283,404.1 50,950.0 1932.4 6405.9 0.0 �38.9 �11,400.0 �363,284.0 �18,145.0

Objective Fn 2

(Rs)

3600.0 5100.0 8150.0 9800.0 9500.0 9400.0 13,300.0 2000.0 3500.0 6500.0 0.0 8000.0 12,000.0 0.0 �9.8 �5784.7 �325.0 �35,000.0

N 55,120,004

Total land 1 1 1 1 1 1 1 1 1 1 1 b 1433.6


Feed 1 0 0 1 2 0 �1 �2 �1 1 N 0

Nutrient �0.045 �0.110 0.030 0.030 �0.045 �0.050 �0.050 �0.045 0.030 0.002 0.002 0.002 1 N 0

Pesticide �0.007 �0.035 �0.035 1 N 0

Fuel 2.51 4.10 3.71 3 1 1 1 N 629.63

Wastelands 1 N 235.1

Non-legumes 1 1 1 1 1 1 b 839.0

Oilseed crops 1 1 1 N 599.3

Oilseed crop ratio 1 �1 �1 N 0

Machine labour �30.0 �117.2 �44.3 �58.6 �30.0 �88.6 �88.6 �60.0 �58.6 �30.0 1 N 0

Vegetable limit 1 1 1 b 807

Animal limit 0 0 �1 0 N 0

Grazing �11 �16 0 �4 �2 0 0 �11 �2 0 200 N 0

Animal labour 1250 140 110 2690 55 65 2535 4720 170 0 b 2,430,000

Human labour 26,147.2 21,096.9 20,312.2 33,746.2 9914.1 38,183.5 54,157.3 72,236.7 225,808.9 283,404.1 50,950.0 1932.4 6405.9 0.0 �38.9 �11,400.0 �363,284.0 �18,145.0







Appendix C

Table 3. Objective functions and constraint matrix for Kundaich

S.Thankappanet

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lEconomics

56(2006)190–208

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